PhD ThesisDistributed Virtual Environments (DVEs) are virtual environments which allow
dispersed users to interact with each other and the virtual world through the
underlying network.
Scalability is a major challenge in building a successful DVE, which is directly
affected by the volume of message exchange. Different techniques have been
deployed to reduce the volume of message exchange in order to support large
numbers of simultaneous participants in a DVE. Interest management is a
popular technique for filtering unnecessary message exchange between users.
The rationale behind interest management is to resolve the "interests" of users
and decide whether messages should be exchanged between them. There are
three basic interest management approaches: region-based, aura-based and
hybrid approaches. However, if the time taken for an interest management
approach to determine interests is greater than the duration of the interaction, it
is not possible to guarantee interactions will occur correctly or at all. This is
termed the Missed Interaction Problem, which all existing interest management
approaches are susceptible to.
This thesis provides a new aura-based interest management approach, termed
Predictive Interest management (PIM), to alleviate the missed interaction
problem. PIM uses an enlarged aura to detect potential aura-intersections and
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initiate message exchange. It utilises variable message exchange frequencies,
proportional to the intersection degree of the objects' expanded auras, to restrict
bandwidth usage. This thesis provides an experimental system, the PIM system,
which couples predictive interest management with the de-centralised server
communication model. It utilises the Common Object Request Broker
Architecture (CORBA) middleware standard to provide an interoperable
middleware for DVEs. Experimental results are provided to demonstrate that
PIM provides a scalable interest management approach which alleviates the
missed interaction problem